Karanasios, S, Allen, D, Norman, AWT et al. (3 more authors) (2013) Making Sense of Digital Traces: An Activity Theory Driven Ontological Approach. Journal of the American Society for Information Science and Technology, 64 (12). 2452 - 2467. ISSN 1532-2882
Abstract
Social web content such as blogs, videos, and other user-generated content present a vast source of rich “digital-traces” of individuals' experiences. The use of digital traces to provide insight into human behavior remains underdeveloped. Recently, ontological approaches have been exploited for tagging and linking digital traces, with progress made in ontology models for well-defined domains. However, the process of conceptualization for ill-defined domains remains challenging, requiring interdisciplinary efforts to understand the main aspects and capture them in a computer processable form. The primary contribution of this article is a theory-driven approach to ontology development that supports semantic augmentation of digital traces. Specifically, we argue that (a) activity theory can be used to develop more insightful conceptual models of ill-defined activities, which (b) can be used to inform the development of an ontology, and (c) that this ontology can be used to guide the semantic augmentation of digital traces for making sense of phenomena. A case study of interpersonal communication is chosen to illustrate the applicability of the proposed multidisciplinary approach. The benefits of the approach are illustrated through an example application, demonstrating how it may be used to assemble and make sense of digital traces.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | (c) 2013, Wiley. This is an author produced version of a paper published in the Journal of the American Society for Information Science and Technology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Design |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Apr 2014 10:23 |
Last Modified: | 18 Jan 2018 04:01 |
Published Version: | http://dx.doi.org/10.1002/asi.22935 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/asi.22935 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:78422 |